The Economist's most reused dataset is not behind the paywall at all. It sits in a public repository, going back to April 2000.
ScrapeIt runs the collection as a managed service. We collect the openly published data, keep every release versioned, join it to the other data you need, and hand back CSV, JSON, Excel or a push into your warehouse.
If you license Economist content, we build within the licence and record its reference on every row.
We do not collect the paywalled site and do not work around its refusal. For the open repository, read its terms with your counsel before any reuse beyond analysis; we do not assert terms we could not find stated.
Big Mac index records carry the date, ISO country code, currency code, country name, local price, dollar exchange rate, dollar price, the raw valuation against the dollar, euro, pound, yen and yuan, the GDP-based adjustment, the adjusted price and the adjusted valuations - exactly as published.
We keep the published values rather than recomputing them. The methodology is public and could be reproduced, but a recomputed index is our number, not The Economist's, and the difference matters to anyone citing it.
Where a client licenses Economist content, article records carry the headline, section, issue date, byline where given and article identifier, within the licence's scope and with its reference on every row.
Citation records, built without the site, show where other outlets cite Economist analysis.
Every row carries the collection timestamp and its source type.
Index versioning is the most useful service on the open data: every published release kept with its date, so revisions become visible and a series can be reproduced exactly as it stood at any point.
Joining the index to macroeconomic data - exchange rates, inflation, wage series from official statistics - is the usual analytical step, and it is cleaner when the index arrives with ISO codes intact, as it is published.
Citation analysis shows where Economist analysis is picked up elsewhere, without collecting the site.
Limits: we do not collect economist.com journalism without a licence, we do not attempt to get past its 403, and we do not use its content for AI purposes against its stated exclusions. We do not assert licence terms for the open repository that we could not find stated.
The Economist is a weekly publication on international affairs, business and economics, known for its analysis and its data journalism. Its journalism sits behind a subscription: requests to the site returned 403, and its crawl rules name the major AI crawlers individually for exclusion.
Its data team, however, publishes some of its work openly. The best known example is the Big Mac index, the burger-based measure of currency valuation, whose full dataset and calculation are published in a public repository.
We checked it directly. The index file goes back to April 2000 and carries, for each country and date, the local price, the dollar exchange rate, the dollar price, the raw index against several major currencies, and an adjusted version that accounts for GDP per person.
That split defines what a project here can be: collection of what the publication chose to open, and licensed work for the rest.
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Requests that name The Economist tend to want one of two things: its data, or its journalism. They have very different answers.
For data, the publication has done part of the work already. The Big Mac index is published openly with its methodology, which means it can be collected, versioned and joined to a client's own economic data without touching the paywall at all. The index is updated periodically, so keeping each published release rather than overwriting it is what allows anyone to see how estimates were revised.
For journalism, the answer is licensing. The site refuses anonymous requests and its crawl rules exclude AI crawlers by name, which leaves no honest route to the articles without the publisher's agreement.
The third point is terms. We did not find a licence file at the repository's standard location, so we do not assert what reuse terms apply. We collect and deliver the published data for analysis, and anything beyond that belongs with your counsel after reading the repository's own terms.
Learn how to use web scraping to solve data problems for your organization
Leveraging advances in technology, the AI-powered web scraper has skyrocketed in demand and is helping to expand capabilities by automating tedious daily tasks and speeding up data collection from thousands of websites several times over.
Web scraping is a method of obtaining web data by extracting it from pages of web resources with the help of a program, that is, in automatic mode. It is used to syntactically convert web pages into more usable forms.
If you specialize in machine learning, you need to feed large amounts of data to the algorithms. Web scraping is the easiest and the most efficient method of collecting the data from all over the Internet.
ScrapeIt is a managed extraction company, not a tool you have to learn. Our team keeps each index release versioned, follows the repository as its structure changes, and keeps published values exactly as published.
You see a sample first, in your format, over the countries and currencies you actually use, so you can check the values against the published file yourself.
Not without a licence. The site refuses anonymous requests with 403 and its crawl rules exclude AI crawlers by name. With a licence we build within its terms; without one, the open data and citation analysis are the honest options.
Some of its data work - most notably the Big Mac index, published with its methodology in a public repository. The index file we checked goes back to April 2000, with raw and GDP-adjusted valuations against five major currencies.
No. We keep the published values. The methodology is public and could be reproduced, but a recomputed index would be our number rather than The Economist's, and that difference matters to anyone citing it.
Because the index is updated periodically and estimates can be revised. Keeping each release with its date lets you reproduce the series exactly as it stood at any point, which overwriting would destroy.
We did not find a licence file at the repository's standard location, so we do not assert one. Read the repository's own terms with your counsel before any reuse beyond analysis.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
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